MOTIVATION: Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a non-random manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis. RESULTS: We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal (PD) axis of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not. AVAILABILITYAND IMPLEMENTATION: Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The importance of environmental fluctuations in the evolution of living organisms by natural selection has been widely noted by biologists and linked to many important characteristics of life such as modularity, plasticity, genotype size, mutation rate, learning, or epigenetic adaptations.In artificial-life simulations, however, environmental fluctuations are usually seen as a nuisance rather than an essential characteristic of evolution.HetCA is a heterogeneous cellular automata characterized by its ability to generate open-ended long-term evolution and "evolutionary progress".In this paper, we propose to measure the impact of different types of environmental fluctuations in HetCA.Our results indicate that environmental changes induce mechanisms analogous to epigenetic adaptation or multilevel selection.This is particularly prevalent in two of the tested fluctuation schemes, which involve a round-robin inhibition of certain cell types, where phenotypic selection seems to occur.

This special issue displays some of the best articles presented at the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALife 14), which was held in New York, USA, on July 30–August 2, 2014 (http://alife2014.alife.org/). ALife 14 was the fourteenth convening of ALife, one of the two largest conference series focusing on the study of artificial life, alternating with ECAL, the European edition.ALife 14 attracted a total of 203 submissions, solicited in two formats: full article (up to 8 pages) and extended abstract (2 pages). Both types were evaluated through multiple peer reviews by the Program Committee. As a result, 102 submissions were accepted for oral presentations and 62 for poster presentations. These articles and abstracts were published in the open-access conference proceedings available from MIT Press' website (https://mitpress.mit.edu/index.php?q=books/artificial-life-14). The conference program was full of scientific, professional, and creative activities, including five superb keynote talks, twenty parallel sessions covering a wide variety of topical areas, a very well-attended poster session with a plenary “Poster Blitz Movie Show,” nine workshops, six tutorials, the First Summer School of the International Society for Artificial Life (ISAL) with four lectures, a science visualization competition, and a career-advising luncheon for postdocs and graduate students. These were four vibrant, intellectually stimulating days in Manhattan.The contributions included in this special issue were selected, by the Organizing Committee and the Best Paper Committee, from the full articles presented orally at the ALife 14 conference, based on their peer review scores and the quality of their presentations. The Best Paper and the Best Student Paper of ALife 14 (http://blogs.cornell.edu/alife14nyc/best-paperposter-awards/) were also included in this special issue. Authors submitted an extended version of their conference manuscript, which went through another round of peer review and revision for journal publication. As a result, there are eight articles included in this special issue. The first four are about artificial chemistry: two discussing abstract models and two addressing real biochemistry. The last four articles are about evolution, two of them also covering morphological adaptation and computing. Their contents are summarized below.The first article, “Complex Autocatalysis in Simple Chemistries,” received the Best Paper award at ALife 14. In this article, Virgo, Ikegami, and McGregor present simple mass-action-kinetics-based artificial chemistry models and demonstrate that, even in a thermodynamically reversible chemical reaction network, complex autocatalytic cycles can emerge when certain direct reactions are prohibited. Complex nonlinear dynamics, such as bistability, arising in their simple models may offer new insight into the origins of life.In “Exploring the Space of Viable Configurations in a Model of Metabolism-Boundary Co-construction,” Agmon, Gates, Churavy, and Beer propose a spatial artificial chemistry model in which autopoietic interaction between boundary formation and metabolic reactions inside it allows stable cellular structures to emerge. They systematically examine the robustness and variability of these cellular structures by subjecting them to global or local perturbations, revealing a transition network of viable configurations.In “Computational Design of a Circular RNA with Prionlike Behavior,” Badelt, Flamm, and Hofacker present a novel theoretical chemistry method to computationally design RNA molecules that have desired properties (energy landscapes), using their software tool called the Vienna RNA package. They demonstrate the effectiveness of the proposed method by using it to design self-replicating RNA molecules that show conformational self-replication, just like the self-replication of prions.In “Compartmentalization of an all-E. coli Cell-Free Expression System for the Construction of a Minimal Cell,” Caschera and Noireaux present an experimental study on the effects of compartmentalization of their cell-free transcription-translation system extracted from E. coli. They show that, when encapsulated in cell-sized lipid vesicles, this system exhibits large fluctuations in gene expression levels, possibly due to heterogeneity in DNA template encapsulation.The next article, “An Informational Study of the Evolution of Codes and of Emerging Concepts in Populations of Agents,” was the recipient of the Best Student Paper award at ALife 14. In this article, Burgos and Polani study an agent population model in which no shared code is initially available for agents' communication and the sources of information are not distinguishable to each agent. Using evolutionary optimization, they demonstrate that a universal code can emerge if the population is well mixed, while multiple distinct codes may result in spatially structured populations. Moreover, “blind” agents can develop concepts about the environment using information coming from other agents.In “A General Statistical Method for Identifying Adaptations by Parameterizing Trait Space,” Blount proposes a formal method to detect adaptation by mapping the traits of evolving organisms to a 3D metric space that consists of variation, heritability, and differential fitness. This method does not require a priori specification of what kind of function or purpose the adaptation possesses. As a proof of concept, the calculation of differential fitness is demonstrated through the application of the proposed method to Packard's Bugs system.In “Population and Evolutionary Dynamics based on Predator-Prey Relationships in a 3D Physical Simulation,” Ito, Pilat, Suzuki, and Arita present a virtual ecosystem of 3D morphological creatures like Sims' blockies, where both ecological and evolutionary dynamics develop simultaneously and spontaneously via coevolution of predator and prey populations. Computer simulations show the emergence of short-term (ecological) and long-term (evolutionary) dynamics occurring in two different regimes of time scales, as well as their mutual interactions.Finally, in “Active Shape Discrimination with Compliant Bodies as Reservoir Computers,” Johnson, Philippides, and Husbands propose to use a randomly generated mass-spring-damper (MSD) network embedded in an agent's body as a “morphogenetic computation” device, which is a physical counterpart of neural-network-based reservoir computing. They demonstrate, through evolutionary search, that such an MSD network can be used successfully as a reservoir computer to control the agent when catching objects of a certain shape while avoiding others.We thank all the authors of the above articles for their excellent contributions, which nicely illustrate the current state of the art of artificial life. We would also like to thank the following people on the Best Paper Committee of ALife 14, who played a crucial role in evaluating numerous articles and selecting the best ones to be included in this volume:• Chair: Luis Rocha• Co-chair: Takashi Ikegami• Committee members: Joshua Auerbach, Lola Cañamero, Dominique Chu, Sylvain Cussat-Blanc, Stéphane Doncieux, Dusan Misevic, Susan Stepney, Sebastian von MammenThe success of the ALife 14 conference was due to all who helped and participated in this event, including the Program Committee members, the five keynote speakers (John H. Conway, Lee Cronin, Naomi Leonard, Jesse Louis-Rosenberg, and Karl Sims), the organizers of workshops and tutorials, local organization staff, and the following sponsors:• US National Science Foundation• MIT Press• Cornell University• Thomas J. Watson School of Engineering and Applied Science at Binghamton University• Wolfram Research• Mary Ann Liebert, Inc. PublishersFinally, we would like to give special thanks to Mark Bedau, the Editor-in-Chief of the Artificial Life journal, and Linda Reedijk, the editorial assistant, for their help, encouragement, and support in this special issue.

We propose an original 2D agent-based model of biological "autopoiesis", the process by which a cell creates and continuously regenerates itself, considered one of the defining characteristics of life.In the space of our simulations, the positions of free molecules are continuous, and polymerized membrane components are regularly arranged (plus noise).While autopoiesis commonly refers to the self-driven maintenance of a system, we also follow Varela's historical study of emergence, and show that the same model can account for both selfperpetuation and self-formation-a step toward uniting the three main perspectives on life: origins, autopoiesis, and replication.Exploring different initial and environmental conditions, we observe that destructive reactions are important for the survival of our autopoietic system, and evaluate their impact on its lifespan.The tendency of cells to form spurious outgrowths is counteracted by moderate decay of the membrane.

This special issue pays tribute to 20 years of artificial life research in Europe. Since its inception around a coffee table in Paris in the summer of 1990, the European Conference on Artificial Life (ECAL) has grown and evolved to incorporate multiple research disciplines that aim to combine the natural and computational sciences. In addition to extending a long and rich tradition in theoretical biology present at that time in Europe toward the computational sciences and engineering, ECAL has also provided a platform for different innovative ideas that have produced a significant impact on other research domains or have resulted in novel research directions with their own workshops and conferences. This influence continues today, with the consideration of the different models and methods that are currently extensively used to answer biological questions in domains such as computational and systems biology. This evolution will benefit the field and will ensure that our models continue to push the envelope as they have been doing for the last two decades.Together with the reviewers and conference chairs of ECAL 2011 in Paris, we selected a small number of especially interesting contributions among the conference's 128 accepted submissions. Their authors were asked to extend their manuscripts for a wider audience and to provide more details than what was allowed by the conference format. The reader can find below a brief description of these articles, which range from artificial chemistry to evolutionary robotics and computational biology. We hope that this selection will provide an incentive for researchers from related fields to pay closer attention to the innovations created within the artificial life community and to motivate researchers already interested in this field to make further advances in the realism and influence of their models. In the following we summarize the topics discussed in this special issue.Xabier Barandiaran and Matthew Egbert study how norms become established from an organism-centered perspective. They give an in-depth analysis and evaluation of a minimal model capable of establishing norms dynamically. The model clearly opens a novel road toward answering questions associated to the notion of minimal agency, which is essential to a wide variety of artificial life models.The work of Navneet Bhalla, Peter Bentley, Peter Vize, and Christian Jacob focuses on the use of staging in the context of physical self-assembling systems made of magnetic shapes. They investigate whether replacing a full mix with gradual addition of morphological information manages to produce more stable constructs for systems composed of elements with heterogeneous properties. They show that this staging methodology can solve complex self-assembly problems by reducing the possibilities of errors in the shape-matching process.Tom Froese, Nathaniel Virgo, and Takashi Ikegami argue in the third article that time scales should play a central role in all models aiming to answer questions linked to the origin of life. Using a reaction-diffusion model, they assert that adaptive behavior realized at intermediate time scales by spatiotemporally localized self-producing systems may have been already present in the beginnings, and conclude with the feasibility of a movement-first approach to the origin of life.In the fourth contribution, Heiko Hamann, Thomas Schmickl, and Karl Crailsheim investigate the relevance of statistical mechanics, and more specifically of the fluctuation theorem, to understanding the collective behavior of simulated swarms. They are able to show that an inverted fluctuation theorem is present in this type of systems, which they claim may be generally applicable to different self-organizing systems studied in artificial life.Tsutomu Oohashi, Tadao Maekawa, Osamu Ueno, Norie Kawai, Emi Nishina, and Manabu Honda investigate a completely different question: Can organisms evolve to give up their immortality? Using an artificial-chemistry-based model called SIVA, they examine the evolution of programmed self-decomposition. Interestingly, they identify the conditions for mortal organisms emerging from immortal ones to outcompete them, which they associate with the emergence of an altruistic activity in terrestrial life.As mentioned earlier, artificial life modeling has a prominent role to play in computational and systems biology. The manuscript of Joshua Payne, Jason Moore, and Andreas Wagner is an example of this cross-fertilization. The authors investigate the space of signal-integration functions, which are defined as functions that map regulatory signals to gene expression states. Their aim is to understand the relationship between the robustness and evolvability of gene expression states when genetic perturbations modify these functions. They show, among other things, that the majority of signal-integration functions are resistant to perturbation.José Pereira, Porfírio Silva, Pedro Lima, and Alcherio Martinoli propose in their work a new framework to coordinate the actions of robot teams using concepts from the field of institutional economics. They first introduce the notion of executable Petri nets (EPNs), which can be run directly on a robot. These EPNs are then used to define institutions and individual robot behaviors, resulting in what they call an institutional agent controller. To validate this new approach, they design some swarm-related experiments in which robots need to coordinate to remain connected over a wireless network, showing that their novel institutional robotics framework can perform as well as some of the other approaches that aim to design swarm behavior, while offering a higher potential for modular control design and consideration of social behavior in robot teams.In the eighth contribution to this special issue, John Rieffel, Davis Knox, Schuyler Smith, and Barry Trimmer provide insight into four years of research into soft robotics. They particularly focus on the question of how to coevolve soft robot morphology and control. They argue that the evolution of soft robots corresponds to solving a problem of three tightly interdependent variables, namely material, morphology and control. In their study, they provide three approaches to address this problem, showing a way to achieve physically embodied soft robots.Within the 20 years of the European Conference of Artificial Life, numerous articles have addressed new evolutionary algorithms designed to solve particular problems or to overcome specific issues in the model originally proposed by John Holland. The ninth and last article, by Nicholas Tomko, Inman Harvey, Nathaniel Virgo, and Andrew Philippides, focuses here on the question of how to improve on prior work about niching and speciation. They suggest a new genetic algorithm (GA), called the group GA, which is based on principles of group or multilevel selection. They show that this new algorithm outperforms some of the earlier works in the same context, and that the group size need not be preset but can itself be evolved.

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.

We propose an evolutionary feature creator (EFC) to explore a non-linear and offline method for generating features in image recognition tasks. Our model aims at extracting low-level features automatically when provided with an arbitrary image database. In this work, we are concerned with the addition of algorithmic depth to a genetic programming (GP) system, hypothesizing that it will improve the capacity for solving problems that require high-level, hierarchical reasoning. For this we introduce a network superstructure that co-evolves with our low-level GP representations. Two approaches are described: the first uses our previously used "shallow" GP system, the second presents a new "deep" GP system that involves this network superstructure. We evaluate these models against a benchmark object recognition database. Results show that the deep structure outperforms the shallow one in generating features that support classification, and does so without requiring significant additional computational time. Further, high accuracy is achieved on the standard ETH-80 classification task, also outperforming many existing specialized techniques. We conclude that our EFC is capable of data-driven extraction of useful features from an object recognition database.

In this work we study open-ended evolution through the analysis of a new model, HetCA, for "heterogeneous cellular automata". Striving for simplicity, HetCA is based on classical two-dimensional CA, but differs from them in several key ways: cells include properties of "age", "decay", and "quiescence"; cells utilize a heterogeneous transition function, one inspired by genetic programming; and there exists a notion of genetic transfer between adjacent cells. The cumulative effect of these changes is the creation of an evolving ecosystem of competing cell colonies. To evaluate the results of our new model, we define a measure of phenotypic diversity on the space of cellular automata. Via this measure, we contrast HetCA to several controls known for their emergent behaviours---homogeneous CA and the Game of Life---and several variants of our model. This analysis demonstrates that HetCA has a capacity for long-term phenotypic dynamics not readily achieved in other models. Runs exceeding one million time steps do not exhibit stagnation or even cyclic behaviour. Further, we show that the design choices are well motivated, as the exclusion of any one of them disrupts the long-term dynamics.

We present a model of parallel co-evolution of development and motion control in soft-bodied, multicellular animats with-out neural networks. Development is guided by an artificial gene regulatory network (GRN), with real-valued expression levels, contained in every cell. Embryos develop within a simulated physics environment and are converted into ani-mat structures by connecting neighboring cells through elas-tic springs. Outer cells, which form the external envelope, are affected by drag forces in a fluid-like environment. Both the developmental program and locomotion controller are en-coded into a single genomic sequence, which consists of reg-ulatory regions and genes expressed into transcription factors and morphogens. We apply a genetic algorithm to evolve in-dividuals able to swim in the simulated fluid, where the fitness depends on distance traveled during the evaluation phase. We obtain various emergent morphologies and types of lo-comotion, some of them showing the use of rudimentary ap-pendages. An analysis of the selected evolved controllers is provided.

While development plays a critical role in the emergence of diversity, its mechanical and chemical actions are considered to be inextricably correlated with genetic control. Since in most extant species the complex growth from zygote to adult organism is orchestrated by a complex gene regulatory network (GRN), the prevalent view is that the evolution of diverse morphologies must result from the evolution of diverse GRN topologies. By contrast, this work focuses on the unique effect of developmental processes through an abstract model of self-regulated structure without genetic regulation - only modulation of initial conditions. Here, morphologies are generated by a simple evolutionary algorithm searching for the longest instances of unfolding dynamics based on tensegrity graphs. The usual regulatory function of the genome is taken over by physical constraints in the graphs, making morphological diversity a pure product of structural complexification. By highlighting the potential of structural development, our model is relevant to both "structuralist" biological models and bio-inspired systems engineering.

Understanding the dynamics of biodiversity has become an important line of research in theoretical ecology and, in particular, conservation biology. However, studying the evolution of ecological communities under traditional modeling approaches based on differential calculus requires species' characteristics to be predefined, which limits the generality of the results. An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. We study here the formation, evolution, and diversity dynamics of a community of virtual plants with a novel individual-centered model involving three different scales: the genetic, the developmental, and the physiological scales. It constitutes an original attempt at combining development, evolution, and population dynamics (based on multi-agent interactions) into one comprehensive, yet simple model. In this world, we observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants. Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions.

On the one hand, phenomena of spontaneous pattern formation are generally random and repetitive, whereas, on the other hand, complicated heterogeneous architectures are the product of human design. Biological organisms are rather unique examples of natural systems that are both self-organized and architectured. Can we export their precise self-formation capabilities to technological systems? To address this issue, I have proposed a new field of research called “Morphogenetic Engineering” [9], which explores the artificial design and implementation of autonomous systems capable of developing complex, heterogeneous morphologies. Particular emphasis is set on the programmability and controllability of self-organization, properties that are often underappreciated in complex systems science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.

Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of "evo-devo" computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.

A commonly held view in evolutionary biology is that speciation (the emergence of genetically distinct and reproductively incompatible subpopulations) is driven by external environmental constraints, such as localized barriers to dispersal or habitat-based variation in selection pressures. We have developed a spatially explicit model of a biological population to study the emergence of spatial and temporal patterns of genetic diversity in the absence of predetermined subpopulation boundaries. We propose a 2-D cellular automata model showing that an initially homogeneous population might spontaneously subdivide into reproductively incompatible species through sheer isolation-by-distance when the viability of offspring decreases as the genomes of parental gametes become increasingly different. This simple implementation of the Dobzhansky-Muller model provides the basis for assessing the process and completion of speciation, which is deemed to occur when there is complete postzygotic isolation between two subpopulations. The model shows an inherent tendency toward spatial self-organization, as has been the case with other spatially explicit models of evolution. A well-mixed version of the model exhibits a relatively stable and unimodal distribution of genetic differences as has been shown with previous models. A much more interesting pattern of temporal waves, however, emerges when the dispersal of individuals is limited to short distances. Each wave represents a subset of comparisons between members of emergent subpopulations diverging from one another, and a subset of these divergences proceeds to the point of speciation. The long-term persistence of diverging subpopulations is the essence of speciation in biological populations, so the rhythmic diversity waves that we have observed suggest an inherent disposition for a population experiencing isolation-by-distance to generate new species.

Several methods for doing intrusion detection have been developed over the years. However, most of these methods are based on crisp statistical techniques that measure deviation from a norm. Due to the wide range of attacks on computers, statistical methods are not always effective because they aggregate many system variables into a single mathematical measure. Instead, taxonomies of attack features based on the concepts of fuzzy logic can be utilized to classify attacks and build simple response rules based on local system variables. Taxonomies however require correct hierarchial construction from subtaxonomies of attack classifiers. An architecture that defines self organizing taxonomies based on fuzzy logic is therefore developed for future investigation.

We propose a spiking neural network for mapping the infinity of schematic visual scenes to a small set of semantic symbols. According to cognitive linguistics, spatial prepositions such as 'in' or 'above' are neutral toward the shape and size of objects. We suggest that they correspond to morphodynamical transforms erasing details and creating virtual structures (boundaries, skeleton). These singularities arise from a large-scale lattice of coupled excitable units exhibiting pattern formation through spatiotemporal order, especially traveling waves. Our model addresses the fundamental cognitive mechanisms of spatial schematization and categorization, which mediate vision and language and are crucial in designing intelligent systems.

Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.